Wavelet-Based Embedded Rate Scalable Still Image Coders: A review

Embedded scalable image coding algorithms based on the wavelet transform have received considerable attention lately in academia and in industry in terms of both coding algorithms and standards activity. In addition to providing a very good coding performance, the embedded coder has the property that the bit stream can be truncated at any point and still decodes a reasonably good image. In this paper we present some state-of-the-art wavelet-based embedded rate scalable still image coders. In addition, the JPEG2000 still image compression standard is presented.

Image Compression Using SPIHT with Modified Spatial Orientation Trees

AbstractA new way of reordering spatial orientation tree of SPIHT for improving compression efficiencies for monochrome and color images has been proposed. Reordering ensures that SPIHT algorithm codes more significant information in the initial bits. List of insignificant pixels and sets are initialized with fewer number of coefficients compared to conventional SPIHT for monochrome images. For color images an altered parent offspring relationship and an extra level of wavelet decomposition on chrominance planes were performed. PSNR improvement of 32.06% was achieved at 0.01 bpp for monochrome images and 19.76% for color images at 0.05 bpp compared to conventional schemes

Mobile Device Use Among Rural, Low-Income Families and the Feasibility of an App to Encourage Preschoolers' Physical Activity: Qualitative Study.

BackgroundAs mobile devices are becoming ubiquitous, technology-based interventions provide a promising strategy to positively influence health behaviors of families with young children. However, questions remain about the feasibility and acceptability of intervention delivery via mobile apps in low-income, rural settings and among families with preschoolers.ObjectiveThe aims of this study were to understand the content and context of mobile device use for preschoolers; explore parent beliefs on this topic, including the acceptability of intervention delivery via mobile devices; and test a prototype of an app to encourage preschoolers' physical activity with both parents and children.MethodsParents (n=29) were recruited from 5 preschool centers in eastern, rural Colorado to complete a semistructured telephone interview regarding preschoolers' mobile device use. A second sample of parents (n=31) was recruited from the same preschool centers to view the app prototype independently and provide feedback. A third sample of preschool children (n=24) was videotaped using the app in small groups to measure engagement and record their responses to the app.ResultsFive key content areas emerged from the telephone interviews: (1) mobile devices are an important part of families' everyday routines, and parents have parameters governing their use; (2) parents often use mobile devices as a tool for behavior management; (3) parents clearly distinguish between mobile device use for learning versus entertainment; (4) parents have an overarching desire for balance in regard to their child's mobile device use; and (5) parents were generally supportive of the idea of using mobile apps for intervention delivery. From the app prototype testing with parents, participants reacted positively to the app and felt that it would be useful in a variety of situations. Testing with preschoolers showed the children were highly engaged with the app and a majority remained standing and/or actively moving through the entire length of the app.ConclusionsMobile devices are already integrated into most families' daily routines and appear to be an acceptable method of intervention delivery in low-income families in rural Colorado. The physical activity app represents an innovative way to reach these families and, with further improvements based on participant feedback, will provide children with a unique opportunity to practice key movement skills

Life Factors And Attendance Rates For Women Enrolled In A Parenting Program

Parenting interventions consistently have been shown to improve positive parenting effectiveness, child adjustment, and family functioning (Gardner et al., 2010). However, attendance rates reported in the literature tend to be low and dropout rates tend to be high, which likely diminishes the positive impact of such programs (Dumas et al., 2007). Parenting group success begins with attendance, therefore, the study aimed to understand which life factors were associated with attendance. Specifically, the study both qualitatively and quantitatively evaluated parents\u27 responses to a brief intervention using MI techniques by using a coding system developed by the author to understand maternal expectations of group, perceived social support, and previous intervention experience. It was expected that mothers\u27 age, race/ethnicity, income, education, marital status, negative affect, motivation, perceived social support, and previous intervention experience would combine to predict their intervention attendance. Data for this thesis were collected by the Mom Power Group developers (Muzik et al., 2011) and provided to the author as an archival data set. In total, 114 mothers and their children were recruited and 99 participated (or completed measures of depression and PTSD). Of the 99, 35 mothers (35%) completed measures with no pre-intervention Motivational Interviewing (MI) questions, and 64 (65%) of the participants completed pre-intervention MI questions and measures. The pre-interview questions were coded using a system designed by the author to classify mothers\u27 expectations for group participation, perception of social support, and experience of previous interventions. Expectations were coded into four categories: help for self, child, parenting, or other. Perceived social support was rated on a four- point scale with zero indicating no support and four indicating strong support for both self and parenting, and coders also briefly summarized the mothers\u27 previous experience and perception of previous experience. Hierarchical linear regressions revealed that race/ethnicity, income, education, marital status, negative affect, motivation, and perceived social support were not significant predictors of attendance, either individually or together after controlling for mothers age and education. Qualitative analyses revealed high levels of positive expectations amongst mothers and that the program offered incentives and removed barriers for maternal participation. This study demonstrated that positive expectations and attendance rates were high for Mom Power Group, and future research is needed to understand the influence of motivational interviewing techniques and incentives on attendance when expectations are low

Fractal image compression and the self-affinity assumption : a stochastic signal modelling perspective

Bibliography: p. 208-225.Fractal image compression is a comparatively new technique which has gained considerable attention in the popular technical press, and more recently in the research literature. The most significant advantages claimed are high reconstruction quality at low coding rates, rapid decoding, and "resolution independence" in the sense that an encoded image may be decoded at a higher resolution than the original. While many of the claims published in the popular technical press are clearly extravagant, it appears from the rapidly growing body of published research that fractal image compression is capable of performance comparable with that of other techniques enjoying the benefit of a considerably more robust theoretical foundation. . So called because of the similarities between the form of image representation and a mechanism widely used in generating deterministic fractal images, fractal compression represents an image by the parameters of a set of affine transforms on image blocks under which the image is approximately invariant. Although the conditions imposed on these transforms may be shown to be sufficient to guarantee that an approximation of the original image can be reconstructed, there is no obvious theoretical reason to expect this to represent an efficient representation for image coding purposes. The usual analogy with vector quantisation, in which each image is considered to be represented in terms of code vectors extracted from the image itself is instructive, but transforms the fundamental problem into one of understanding why this construction results in an efficient codebook. The signal property required for such a codebook to be effective, termed "self-affinity", is poorly understood. A stochastic signal model based examination of this property is the primary contribution of this dissertation. The most significant findings (subject to some important restrictions} are that "self-affinity" is not a natural consequence of common statistical assumptions but requires particular conditions which are inadequately characterised by second order statistics, and that "natural" images are only marginally "self-affine", to the extent that fractal image compression is effective, but not more so than comparable standard vector quantisation techniques

Significance linked connected component analysis plus

Dr. Xinhua Zhuang, Dissertation Supervisor.Field of Study: Computer Science."May 2018."An image coding algorithm, SLCCA Plus, is introduced in this dissertation. SLCCA Plus is a wavelet-based subband coding method. In wavelet-based subband coding, the input images will go through a wavelet transform and be decomposed into wavelet subband pyramids. Then the characteristics of the wavelet coefficients within and among subbands will be utilized to removing the redundancy. The rest information will be organized and go through entropy encoding. SLCCA Plus contains a series improvement method to the SLCCA. Before SLCCA, there are three top-ranked wavelet image coders. Namely, Embedded Zerotree Wavelet coder (EZW), Morphological Representation of Wavelet Date (MEWD), and Set Partitioning in Hierarchical Trees (SPIHT). They exploit either inter-subband relation among zero wavelet coefficients or within-subband clustering. SLCCA, on the other hand, outperforms these three coders by exploring both the inter- subband coefficients relations and within-subband clustering of significant wavelet coefficients. SLCCA Plus strengthens SLCCA in the following aspects: Intelligence quantization, enhanced cluster filter, potential-significant shared-zero, and improved context models. The purpose of the first three improvements is to remove redundancy information further while keeping the image error as low as possible. As a result, they achieve a better trade-off between bit cost and image quality. Moreover, the improved context lowers the entropy by refining the classification of symbols in cluster sequence and magnitude bit-planes. Lower entropy means the adaptive arithmetic coding can achieve a better coding gain. For performance evaluation, SLCCA Plus is compared to SLCCA and JPEG2000. On average, SLCCA Plus achieves 7% bit saving over JPEG 2000 and 4% over SLCCA. The results comparison shows that SLCCA Plus shows more texture and edge details at a lower bitrate.Includes bibliographical references (pages 88-92)

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Visual perception based bit allocation for low bitrate video coding

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 45-47).by Rajesh Suryadevara.M.S